This study investigates the stability of hydrological drought trends in the Euphrates Basin from 1960 to 2020 using three-dimensional (3D) graphical representations based on innovative trend analysis (ITA) and triple Wilcoxon test (WT) methods. Unlike traditional ITA and WT, which are widely used for trend identification but do not inherently provide trend stability information, this study employs a novel approach to assess and visualize trend stability. The Triple WT method divides the data into three equal segments, examining differences without altering the time series. Drought indices are calculated for 3-month, 6-month, and 12-month time scales using historical streamflow data from five stations. The research identifies trends and their stabilities across three distinct periods: 1967–1984, 1985–2002, and 2003–2020. Results show that as the time scale increases, trend differences between extreme drought conditions diminish. One station consistently exhibits significantly decreasing trends, while three stations show unstable trends with notable variations in the standardized streamflow index (SSFI). The use of 3D-ITA and Triple WT effectively captures the dynamics and stability of drought trends, offering a deeper understanding of hydrological drought in the Euphrates Basin. These findings provide a reference for future studies on drought trend mechanisms in various climatic regions.

Climate change and agricultural activities are significant sources of stress to the natural environment and water resources. These also affect erosion and the associated estimation of sediment yields, which is also a crucial task in the hydrological models. The presented study is significant for the development of sustainable watershed management practices. It also aims to determine the effects of climate change and different agricultural best management practices (BMPs) on the sediment loads of the North Aegean Basin in Türkiye by using the Soil and Water Assessment Tool (SWAT) model. While sediment calibration was performed for 2014, streamflow calibration and verification were performed using the SWAT Calibration and Uncertainty Program (SWAT-CUP) for the period 2012–2013 and 2014–2015, respectively. The obtained results showed that the climate change scenarios reduce the surface waters of the basin and sediment yield in accordance with the hydrological transport processes. During the 2012–2030 time period, runoff in the basin for the RCP4.5 and RCP8.5 climate change scenarios decreased by 38.5% and 31.8%, respectively, and the basin sediment yield decreased by 55.7% and 50.7%, respectively. The sediment yields to water resources had distinctive reductions due to BMPs such as zero tillage, vertical tillage, cover crop, and terracing. Considering the RCP4.5 and RCP8.5 scenarios, BMPs reduced the sediment yield in the range of 0.93–4.03% and 0.89–3.85%, respectively. Determining the sediment transport by using hydrological modeling and the effects of climate change for different agricultural practices on erosion will be useful for decision-makers.

Understanding the persistence and stability of hydroclimatic trends is essential for climate adaptation and sustainable water resource management, particularly in Mediterranean regions characterized by irregular precipitation regimes. This study examines long-term rainfall variability (1974–2021) at six meteorological stations along the southern coasts of Türkiye using three complementary non-parametric techniques: the Mann-Kendall (MK) test, the Wilcoxon Signed-Rank Test (WT), and the Innovative Trend Analysis (ITA). The three tests were applied with their respective methodological extensions to enhance sensitivity and better capture trend stability. Results show that while most stations exhibit generally stable rainfall regimes, period- and location-specific variations with non-monotonic or oscillatory tendencies are present, revealing patterns that standard trend tests often fail to detect. The WT method was more responsive to short-term fluctuations, whereas ITA and its three-dimensional version (3D-ITA) provided valuable insights into trend persistence and stability. Overall, the findings highlight that trend stability assessment enables the distinction between transient climate variability and sustained hydroclimatic change, offering a stronger scientific basis for adaptive water management and regional sustainability planning under climate uncertainty.

Beyşehir Lake is the largest freshwater lake in the Mediterranean region of Turkey that is used for drinking and irrigation purposes. The aim of this paper is to examine the potential for data-driven methods to predict long-term lake levels. The surface water level variability was forecast using conventional machine learning models, including autoregressive moving average (ARMA), autoregressive integrated moving average (ARIMA), and seasonal autoregressive integrated moving average (SARIMA). Based on the monthly water levels of Beyşehir Lake from 1992 to 2016, future water levels were predicted up to 24 months in advance. Water level predictions were obtained using conventional time series stochastic models, including autoregressive moving average, autoregressive integrated moving average, and seasonal autoregressive integrated moving average. Using historical records from the same period, prediction models for precipitation and evaporation were also developed. In order to assess the model’s accuracy, statistical performance metrics were applied. The results indicated that the seasonal autoregressive integrated moving average model outperformed all other models for lake level, precipitation, and evaporation prediction. The obtained results suggested the importance of incorporating the seasonality component for climate predictions in the region. The findings of this study demonstrated that simple stochastic models are effective in predicting the temporal evolution of hydrometeorological variables and fluctuations in lake water levels.